Package: brulee 1.1.0
Max Kuhn
brulee: High-Level Modeling Functions with 'torch'
Provides high-level modeling functions to define and train models using the 'torch' R package. Models include linear, logistic, and multinomial regression as well as multilayer perceptrons.
Authors:
brulee_1.1.0.tar.gz
brulee_1.1.0.tar.gz(r-4.7-any)brulee_1.1.0.tar.gz(r-4.6-any)
brulee_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
brulee/json (API)
| # Install 'brulee' in R: |
| install.packages('brulee', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tidymodels/brulee/issues
Pkgdown/docs site:https://brulee.tidymodels.org
Last updated from:36e7c38674. Checks:4 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 208 | ||
| source / vignettes | OK | 204 | ||
| linux-release-x86_64 | OK | 215 | ||
| wasm-release | OK | 164 |
Exports:autoplotbrulee_activationsbrulee_auto_intbrulee_chronosbrulee_linear_regbrulee_logistic_regbrulee_mlpbrulee_mlp_two_layerbrulee_multinomial_regbrulee_resnetbrulee_rlnbrulee_saintbrulee_tab_iclcoefmatrix_to_datasetschedule_cyclicschedule_decay_exposchedule_decay_timeschedule_stepset_learn_ratetab_icl_download_weightstab_icl_weights_availabletunable
Dependencies:bitbit64callrclicorocpp11curldescdplyrfarvergenericsggplot2gluegtablehardhatisobandjsonlitelabelinglifecyclemagrittrotelpillarpkgconfigprocessxpspurrrR6RColorBrewerRcpprlangS7safetensorsscalessparsevctrstibbletidyselecttorchutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Activation functions for neural networks in brulee | brulee_activations |
| Fit AutoInt models for tabular data | brulee_auto_int brulee_auto_int.data.frame brulee_auto_int.default brulee_auto_int.formula brulee_auto_int.matrix brulee_auto_int.recipe |
| Chronos-2 pretrained forecasting model | brulee_chronos brulee_chronos.data.frame brulee_chronos.default brulee_chronos.formula brulee_chronos.recipe |
| Fit a linear regression model | brulee_linear_reg brulee_linear_reg.data.frame brulee_linear_reg.default brulee_linear_reg.formula brulee_linear_reg.matrix brulee_linear_reg.recipe |
| Fit a logistic regression model | brulee_logistic_reg brulee_logistic_reg.data.frame brulee_logistic_reg.default brulee_logistic_reg.formula brulee_logistic_reg.matrix brulee_logistic_reg.recipe |
| Fit neural networks | brulee_mlp brulee_mlp.data.frame brulee_mlp.default brulee_mlp.formula brulee_mlp.matrix brulee_mlp.recipe brulee_mlp_two_layer brulee_mlp_two_layer.data.frame brulee_mlp_two_layer.default brulee_mlp_two_layer.formula brulee_mlp_two_layer.matrix brulee_mlp_two_layer.recipe |
| Fit a multinomial regression model | brulee_multinomial_reg brulee_multinomial_reg.data.frame brulee_multinomial_reg.default brulee_multinomial_reg.formula brulee_multinomial_reg.matrix brulee_multinomial_reg.recipe |
| Fit residual neural networks (ResNet) | brulee_resnet brulee_resnet.data.frame brulee_resnet.default brulee_resnet.formula brulee_resnet.matrix brulee_resnet.recipe |
| Fit Regularization Learning Networks (RLN) | brulee_rln brulee_rln.data.frame brulee_rln.default brulee_rln.formula brulee_rln.matrix brulee_rln.recipe |
| Fit SAINT models for tabular data | brulee_saint brulee_saint.data.frame brulee_saint.default brulee_saint.formula brulee_saint.matrix brulee_saint.recipe |
| Fit a TabICL tabular foundation model | brulee_tab_icl brulee_tab_icl.data.frame brulee_tab_icl.default brulee_tab_icl.formula brulee_tab_icl.matrix brulee_tab_icl.recipe |
| Plot model loss over epochs | autoplot.brulee_auto_int autoplot.brulee_linear_reg autoplot.brulee_logistic_reg autoplot.brulee_mlp autoplot.brulee_multinomial_reg autoplot.brulee_resnet autoplot.brulee_rln autoplot.brulee_saint brulee-autoplot |
| Extract Model Coefficients | brulee-coefs coef.brulee_linear_reg coef.brulee_logistic_reg coef.brulee_mlp coef.brulee_multinomial_reg coef.brulee_resnet coef.brulee_rln |
| Convert data to torch format | matrix_to_dataset |
| Predict from a 'brulee_auto_int' | predict.brulee_auto_int |
| Predict from a 'brulee_chronos' model | predict.brulee_chronos |
| Predict from a 'brulee_linear_reg' | predict.brulee_linear_reg |
| Predict from a 'brulee_logistic_reg' | predict.brulee_logistic_reg |
| Predict from a 'brulee_mlp' | predict.brulee_mlp |
| Predict from a 'brulee_multinomial_reg' | predict.brulee_multinomial_reg |
| Predict from a 'brulee_resnet' | predict.brulee_resnet |
| Predict from a 'brulee_rln' | predict.brulee_rln |
| Predict from a 'brulee_saint' | predict.brulee_saint |
| Predict from a 'brulee_tab_icl' | predict.brulee_tab_icl |
| Change the learning rate over time | schedule_cyclic schedule_decay_expo schedule_decay_time schedule_step set_learn_rate |
| Summarize the architecture of a brulee model | summary.brulee summary.brulee_auto_int summary.brulee_mlp summary.brulee_resnet summary.brulee_rln summary.brulee_saint |
| Download and cache pretrained TabICL weights | tab_icl_download_weights tab_icl_weights_available |
| Training Efficiency | training_efficiency |
